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AMD and Stability AI launch the industry's first Stable Diffusion 3.0 Medium AI model optimized for AMD's XDNA 2 NPUs, designed to run locally on Ryzen laptops (Anton Shilov/Tom's Hardware)

AMD unveils industry-first Stable Diffusion 3.0 Medium AI model generator tailored for XDNA 2 NPUs — designed to run locally on Ryzen AI laptops
An offline image generator for XDNA 2 NPUs.

DiffusionNFT: Online Diffusion Reinforcement with Forward Process
Kaiwen Zheng, Huayu Chen, Haotian Ye, Haoxiang Wang, Qinsheng Zhang, Kai Jiang, Hang Su, Stefano Ermon, Jun Zhu, Ming-Yu Liu
https://arxiv.org/abs/2509.16117

DiffusionNFT: Online Diffusion Reinforcement with Forward Process
Online reinforcement learning (RL) has been central to post-training language models, but its extension to diffusion models remains challenging due to intractable likelihoods. Recent works discretize the reverse sampling process to enable GRPO-style training, yet they inherit fundamental drawbacks, including solver restrictions, forward-reverse inconsistency, and complicated integration with classifier-free guidance (CFG). We introduce Diffusion Negative-aware FineTuning (DiffusionNFT), a new o…

heise | macOS: Stable Diffusion in die eigene App integrieren
Ohne Training, aber mit dem vollen Potenzial von Apples Hardware: So binden Sie den Text-zu-Bild-Generator in Ihre eigene App ein.
https://www.

Warum eben nicht alle »irgendwie ein bisschen autistisch« sind.
https://www.instagram.com/reel/DNBoN61vGDq/?utm_source=ig_web_copy_link

Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising
Shen Cheng, Haipeng Li, Haibin Huang, Xiaohong Liu, Shuaicheng Liu
https://arxiv.org/abs/2509.16091 https://…

Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising
In this work, we present Blind-Spot Guided Diffusion, a novel self-supervised framework for real-world image denoising. Our approach addresses two major challenges: the limitations of blind-spot networks (BSNs), which often sacrifice local detail and introduce pixel discontinuities due to spatial independence assumptions, and the difficulty of adapting diffusion models to self-supervised denoising. We propose a dual-branch diffusion framework that combines a BSN-based diffusion branch, generati…

2025 AFC Division Odds: Can Chiefs, Bills, Ravens, Texans Maintain Streaks? https://www.foxsports.com/stories/nfl/2025-afc-division-odds

NFC West betting preview: 49ers and Rams top odds, but don't count out Seahawks and Cards https://www.espn.com/espn/betting/story/_/id/46036810/2025-nfc-west-division-betting-nfl-preview-favorites…

Dream 7B: Diffusion Large Language Models
Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, Lingpeng Kong
https://arxiv.org/abs/2508.15487 https:/…

Dream 7B: Diffusion Large Language Models
We introduce Dream 7B, the most powerful open diffusion large language model to date. Unlike autoregressive (AR) models that generate tokens sequentially, Dream 7B employs discrete diffusion modeling to refine sequences in parallel through iterative denoising. Our model consistently outperforms existing diffusion language models on general, mathematical, and coding tasks. Dream 7B demonstrates superior planning abilities and inference flexibility, including arbitrary-order generation, infilling…

Inference Time Debiasing Concepts in Diffusion Models
Lucas S. Kupssinsk\"u, Marco N. Bochernitsan, Jordan Kopper, Ot\'avio Parraga, Rodrigo C. Barros
https://arxiv.org/abs/2508.14933

Inference Time Debiasing Concepts in Diffusion Models
We propose DeCoDi, a debiasing procedure for text-to-image diffusion-based models that changes the inference procedure, does not significantly change image quality, has negligible compute overhead, and can be applied in any diffusion-based image generation model. DeCoDi changes the diffusion process to avoid latent dimension regions of biased concepts. While most deep learning debiasing methods require complex or compute-intensive interventions, our method is designed to change only the inferen…

heise | Vom Text zum Bild: So funktioniert Stable Diffusion
Das frei verfügbare Stable Diffusion generiert aus Texten realitätsnahe und detailreiche Bilder. Wir erklären, wie die KI arbeitet.
https://www.